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Damage localization based on ambient vibration data in combination with finite element models can be challenging, in particular due to the large number of parameters in the model and noisy measurement data. Changes in different structural parameters can cause similar changes in data-driven features, and vice versa, it can be challenging to identify which parameter caused the deviation in the data. The problem is ill-conditioned and slight variations in the features, due to inherent statistical uncertainty, can lead to significant errors in the result interpretation. A possible solution is sensitivity-based statistical tests in combination with a parameter clustering approach that considers the uncertainties of data-driven features. In this context, this paper introduces the concept of damage localizability , and provides a framework to evaluate it based on the minimum detectable parameter changes, possible false alarms in unchanged parameters, as well as the achievable damage localization resolution. Since clustering approaches depend on user-defined hyperparameters, such as the number of clusters, the second objective of this paper is to optimize the performance of the damage localization, by adjusting the hyperparameters for clustering. A particular strength of the approach is that the analysis can be conducted based on data and a numerical model from the undamaged structure alone, making it a suitable approach to assess and to optimize the diagnosis performance before damage occurs. For proof of concept, a laboratory case study on a simply-supported steel beam is presented, where the localizability of mass changes is analyzed and optimized. • An approach is developed to assess and optimize the performance of damage localization tests. • Clustering of sensitivity vectors avoids ill-conditioning. • Localizability is defined by the three criteria: resolution, detectability, and false alarms. • All criteria can be quantified based on data from undamaged structures, and a numerical model. • Experimental data confirm the predictive capabilities of the performance assessment. [ABSTRACT FROM AUTHOR]

• Complex modal parameter identification under ambient excitation is studied. • A Bayesian strategy is adopted to find the uncertainty in identified parameters. • Objective function gradient is employed to make the optimization efficient. • Phased optimization technique is prescribed for better convergence. • An experimental study is included to investigate the applicability of the method. In this work, we present a Bayesian time–domain method for identifying the complex modal parameters of a linear dynamic system from ambient vibration data. Complex modal parameter identification is valid for both classically and non-classically damped systems, in contrast to real modal parameter identification, which is exclusively applicable to classically damped systems. A Bayesian method is adopted here which makes it possible to determine the joint posterior Probability Density Function (PDF) of the modal parameters for given measured data and modeling assumptions. For sufficient data, it is seen that the modal parameters are globally identifiable, and the posterior PDF is approximated by a Gaussian distribution with mean at the optimal parameters that maximize the posterior PDF. The uncertainty in the identified modal parameters is given by the covariance matrix of the posterior PDF. Analytical expressions are derived for computing the gradient for efficient optimization and the Hessian for determining the covariance matrix. Further, it is shown that the assumption of classical damping during analysis results in poor prediction of damping ratios in the case of non-classically damped systems. The application of the proposed approach is illustrated by means of simulated examples and an experimental study. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic modal analysis approach for signals of high-rise buildings recorded under real-world ambient excitations. The fact of working over such type of signals is faced with several challenges: the time-domain convolution between the system impulse response and the seismic noise, the existence of several components, the presence of closely-spaced frequency modes, with high additive noises, and low, exponential and damped amplitudes. The proposed approach handles these challenges simultaneously without the need for a user intervention. It is based on a filter-free Random Decrement Technique to estimate the free-decay response, followed by a spectral-based method for a rough modal estimate and finalized by a Maximum-Likelihood Estimation process to refine the modal estimates. Each of these processes is responsible to tackle one or more of the aforementioned challenges in the aim to provide an automatic and moreover a reliable modal analysis of the studied signals. [ABSTRACT FROM AUTHOR]

In the last few decades large research efforts have been devoted to the development of methods for automated detection of damage and degradation phenomena at an early stage. Modal-based damage detection techniques are well-established methods, whose effectiveness for Level 1 (existence) and Level 2 (location) damage detection is demonstrated by several studies. The indirect estimation of tensile loads in cables and tie-rods is another attractive application of vibration measurements. It provides interesting opportunities for cheap and fast quality checks in the construction phase, as well as for safety evaluations and structural maintenance over the structure lifespan. However, the lack of automated modal identification and tracking procedures has been for long a relevant drawback to the extensive application of the above-mentioned techniques in the engineering practice. An increasing number of field applications of modal-based structural health and performance assessment are appearing after the development of several automated output-only modal identification procedures in the last few years. Nevertheless, additional efforts are still needed to enhance the robustness of automated modal identification algorithms, control the computational efforts and improve the reliability of modal parameter estimates (in particular, damping). This paper deals with an original algorithm for automated output-only modal parameter estimation. Particular emphasis is given to the extensive validation of the algorithm based on simulated and real datasets in view of continuous monitoring applications. The results point out that the algorithm is fairly robust and demonstrate its ability to provide accurate and precise estimates of the modal parameters, including damping ratios. As a result, it has been used to develop systems for vibration-based estimation of tensile loads in cables and tie-rods. Promising results have been achieved for non-destructive testing as well as continuous monitoring purposes. They are documented in the last sections of the paper. [ABSTRACT FROM AUTHOR]

Damage diagnosis based on global structural vibrations critically depends on the sensor layout, in particular when a small number of sensors is used for large structures under unknown excitation. This paper proposes a sensor placement strategy that yields an optimized sensor layout with maximum damage detectability in selected structural components. The optimization criterion is based on the Fisher information, which quantifies the information that the damage-sensitive feature carries on the design parameters of structural components, such as material constants or cross-sectional values. It is evaluated using a finite element model, and considers the statistical uncertainties of the damage-sensitive feature. The methodology is shown for the stochastic subspace-based damage detection method, but can be applied to any damage-sensitive feature whose distribution can be approximated as Gaussian. It is suitable to find the optimal layout for a fixed number of sensors and to choose an appropriate number of sensors. Since the Fisher information is defined component-wise, the sensor layout can be tuned to become more sensitive to damage in local structural components, such as damage hotspots, non-inspectable components, or components that are critical for the safety and serviceability of the structure. For proof of concept, the sensor layout on a laboratory beam is optimized based on numerical simulations, and it is showcased that the optimal sensor layout leads to the highest damage detectability for experimental data. • Gaussian vibration-based features are employed for statistical damage detection. • The sensor layout is optimized to maximize the feature's sensitivity and minimize its uncertainty. • The optimization criterion allows one to tune the sensor layout for high detectability in critical components. • A custom-made genetic algorithm efficiently finds a close-to-optimal solution for large structures. • The optimization is based on a numerical model and validated in a lab experiment. [ABSTRACT FROM AUTHOR]

With the increasing application of long-term dynamic monitoring systems to civil engineering structures, automated operational modal analysis (OMA) has become a fascinating research area for structural health monitoring. In recent years, different automated approaches based on the existing parametric or nonparametric OMA algorithms have been developed. However, they usually perform less well in large complex structures with weak excitation because of inherent limitations of those algorithms. In this work, a three-stage automated OMA algorithm based on a combination of the second-order blind identification (SOBI) and the covariance-driven stochastic subspace identification (SSI-COV) is proposed, which takes full advantage of both parametric and nonparametric algorithms while overcoming the limitations. The automated approach avoids the ambiguous work related to interpreting stabilization diagrams without losing accuracy and thus obtains more reliable modal parameters. The proposed method is fully validated by simulated data from an 8-DOF system and data recorded on a concrete arch-gravity dam in view of single and continuous monitoring. The results show that all physical modes can be well-separated and reliably identified by the proposed method with high accuracy, confirming the excellent performance of the method, especially for continuous monitoring. [ABSTRACT FROM AUTHOR]

• The minimum detectable change in structural parameters is determined for damage detection. • The analysis requires vibration data from the reference state and a finite element model. • Detectability is defined through a reliability concept for monitoring using statistical tests. • Detectable damages explicitly depend on damage sensitivity, noise, data length and reliability. • The predicted damage detectability is confirmed on experimental data of a beam with added masses. This paper derives a formula to determine the minimum detectable damage based on ambient vibration data. It is a key element to analyze which damage scenarios can be detected before a monitoring system is installed. For the analysis, vibration data from the reference structure as well as a finite element model are required. Minimum detectability is defined by adopting a code-based reliability concept that considers the probability of detection and the probability of false alarms. The results demonstrate that the minimum detectable damage depends on three elements: the uncertainty of the damage-sensitive feature (which decreases with increasing measurement duration), its sensitivity towards model-based design parameters, and the reliability requirements regarding the damage diagnosis results. The theory is developed for the stochastic subspace-based damage detection method but can be applied to any damage-sensitive feature provided its sensitivities and statistical properties can be characterized. For proof of concept, the minimum detectable change in stiffness and mass of a pin-supported beam are analyzed in a numerical and experimental study, respectively. The predictions with the developed approach appear to be accurate and robust to noise effects for both simulated and real data. [ABSTRACT FROM AUTHOR]

• A double jump phenomenon appears in the frequency domain near internal resonances. • The nonlinear behavior is successfully exploited for increasing operating bandwidth. • Deliverable average power is enough for a vast range of sensor-based applications. • Compactness of the design permits incorporation inside vibrational structures. The steady state response of a particular harvesting system is investigated under the condition of external and internal resonance, with emphasis on the double jump phenomenon. Following the trend of recent researches on exploring nonlinearity aspects for broadening the frequency resonance bandwidth, the problem of effective energy harvesting from a broadband source is dealt with. The proposed harvester can be embedded within vibrational structures and can dually act as a vibration absorber. The governing non-linear partial differential equations are truncated into a set of perturbed equations via Galerkin method. The method of multiple scales is applied to derive the modulated amplitude versus frequency at the vicinity of flapwise and chordwise primary resonances, as well as around other internal resonance frequencies. The amplitude-frequency response plot reveals resonance peaks bending to the left and right, i.e., splitting into two different tongues in contrast to conventional jumps which lean only toward higher or lower frequency directions. Numerical results demonstrate that this internal resonance-based harvesting design can produce sufficient power for the consumption of typical MEMS devices. [ABSTRACT FROM AUTHOR]